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Rejection control and sequential importance sampling.
Blitzstein, J. & Diaconis, P. A sequential importance sampling algorithm for generating random graphs with prescribed degrees.
J. Blitzstein, P. Diaconis (2011) "A sequential importance sampling algorithm for generating random graphs with prescribed degrees", Internet Mathematics.
In this paper, a Bayesian self-calibration approach using sequential importance sampling (SIS) is proposed.
Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature.
In this paper, we propose estimation of the domain integral through application of a method termed sequential importance sampling (SIS).
The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function.
In particular, we present a filtering reformulation of global optimization in terms of a special case of sequential importance sampling methods called particle filters.
This paper proposes the application of sequential importance sampling (SIS) to the estimation of the probability of failure in structural reliability.
The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-à-vis results from a form of sequential importance sampling filter.
Thus, a stochastic version of an expectation conditional maximization (ECM) algorithm was adopted, where the E-step was approximated by sequential importance sampling with resampling (SISR).
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